License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICCSW.2018.6
URN: urn:nbn:de:0030-drops-101872
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10187/
Go to the corresponding OASIcs Volume Portal

Chiroma, Fatima ; Cocea, Mihaela ; Liu, Han

Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification

pdf-format:
OASIcs-ICCSW-2018-6.pdf (0.3 MB)

Abstract

Feature selection is typically employed before or in conjunction with classification algorithms to reduce the feature dimensionality and improve the classification performance, as well as reduce processing time. While particular approaches have been developed for feature selection, such as filter and wrapper approaches, some algorithms perform feature selection through their learning strategy. In this paper, we are investigating the effect of the implicit feature selection of the PRISM algorithm, which is rule-based, when compared with the wrapper feature selection approach employing four popular algorithms: decision trees, naïve bayes, k-nearest neighbors and support vector machine. Moreover, we investigate the performance of the algorithms on target classes, i.e. where the aim is to identify one or more phenomena and distinguish them from their absence (i.e. non-target classes), such as when identifying benign and malign cancer (two target classes) vs. non-cancer (the non-target class).

BibTeX - Entry

@InProceedings{chiroma_et_al:OASIcs:2019:10187,
  author =	{Fatima Chiroma and Mihaela Cocea and Han Liu},
  title =	{{Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification}},
  booktitle =	{2018 Imperial College Computing Student Workshop (ICCSW 2018)},
  pages =	{6:1--6:6},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-097-2},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{66},
  editor =	{Edoardo Pirovano and Eva Graversen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10187},
  URN =		{urn:nbn:de:0030-drops-101872},
  doi =		{10.4230/OASIcs.ICCSW.2018.6},
  annote =	{Keywords: Feature Selection, Prism, Rule-based Learning, Wrapper Approach}
}

Keywords: Feature Selection, Prism, Rule-based Learning, Wrapper Approach
Collection: 2018 Imperial College Computing Student Workshop (ICCSW 2018)
Issue Date: 2019
Date of publication: 25.01.2019

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI